Quantization of channel state information in multiple antenna systems

ABSTRACT

We propose a new vector quantization (VQ) algorithm for reducing bit rate used for channel state information feedback in a variety of multiple antenna systems on flat and frequency selective channels. 
     We consider an approach where the receiver chooses an instantaneous capacity maximizing entry from a small finite set of predefined covariance matrices. The codebook of covariance matrices is constructed based on separate optimization of the channel eigenmodes and power division between them. If, for the given channel realization, one of the predefined covariance matrices provides higher capacity than equal power distribution the indexes of the two codebooks are fed to the transmitter, otherwise, the transmitter uses the open-loop approach. 
     We implement proposed algorithm on flat fading and frequency selective MIMO channels and show the influence of the feedback rate on system capacity. For a case of flat fading, required feedback rates are approximately equal to the product of the number of transmit and receive antennas. Although much higher feedback throughput is needed for reliable channel state information feedback in OFDM systems, we show a simple clustering technique lowering the required bit rate. 
     Moreover, we propose a new vector quantization (VQ) algorithm for encoding channel state information feedback in multiple antenna, multi-user systems operating on flat fading channels with rich scattering. 
     We consider an approach where the receiver chooses an instantaneous throughput maximizing modulation matrix from a finite set of predefined matrices (code-words). The codebook of modulation matrices is constructed based on joint optimization of the dominant channel eigenmodes of users and separate quantization of power levels. 
     The proposed algorithm is very flexible and can be used in a variety of system configurations, including varying number of receiver antennas and frequency selective channels. We implement the proposed algorithm on flat fading MIMO channels and show the influence of the feedback rate on system capacity. We demonstrate that, even with low feedback rate, the ergodic capacity of the proposed system closely approaches the theoretic capacity of the system with perfect channel state information at the transmitter.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 USC 119(e) of provisional patent application No. 60/808,806 filed May 26, 2006.

BACKGROUND

The development of the modern Internet-based data communication systems and ever increasing demand for bandwidth have spurred an unprecedented progress in development of high capacity wireless systems. The major trends in such systems design are the use of multiple antennas to provide capacity gains on fading channels and orthogonal frequency division multiplexing (OFDM) to facilitate the utilization of these capacity gains on rich scattering frequency-selective channels. Since the end of the last decade, there has been an explosion of interest in multiple-input multiple-output systems (MIMO) and a lot of research work has been devoted to their performance limits and methods to achieve them.

One of the fundamental issues in multiple antenna systems is the availability of the channel state information at transmitter and receiver. While it is usually assumed that perfect channel state information (CSI) is available at the receiver, the transmitter may have perfect, partial or no CSI. In case of the single user systems, the perfect CSI at the transmitter (CSIT) allows for use of a spatial water-filling approach to achieve maximum capacity. In case of multi-user broadcast channels (the downlink), the capacity is maximized by using the so called dirty paper coding, which also depends on the availability of perfect CSIT. Such systems are usually refereed to as closed-loop as opposed to open-loop systems where there is no feedback from the receiver and the transmitter typically uses equal-power division between the antennas.

In practice, the CSI should be quantized to minimize feedback rate while providing satisfactory performance of the system. The problem has attracted attention of the scientific community and papers provided solutions for beamforming on flat-fading MEMO channels where the diversity gain is the main focus. Moreover, some authors dealt with frequency-selective channels and OFDM modulation although also those papers were mainly devoted to beamforming approach.

Unfortunately, availability of full CSIT is unrealistic due to the feedback delay and noise, channel estimation errors and limited feedback bandwidth, which forces CSI to be quantized at the receiver to minimize feedback rate. The problem has attracted attention of the scientific community and papers have provided solutions for single-user beamforming on flat-fading MIMO channels, where the diversity gain is the main focus. More recently, CSI quantization results were shown for multi-user zero-forcing algortihms by Jindal.

SUMMARY

We present a simple, flexible algorithm that is constructed with multiplexing approach to MIMO transmission, i.e., where the channel is used to transmit multiple data streams. We use a vector quantizer approach to construct codebooks of water-filling covariance matrices which can be used in a wide variety of system configurations and on frequency selective channels. Moreover, we propose a solution which reduces the required average feedback rate by transmitting the indexes of only those covariance matrices which provide higher instantaneous capacity than the equal power allocation.

BRIEF DESCRIPTION OF THE FIGURES

Embodiments will now be described with reference to the figures, in which like reference characters denote like elements, by way of example, and in which:

FIG. 1 shows a typical transmission system;

FIG. 2 is a flow diagram showing basic system operation;

FIG. 3 is a flow diagram showing use of quantized mode gain in the system of FIG. 1;

FIG. 4 a is a flow diagram showing design of optimized orthogonal mode matrices for use in the system of FIG. 1 for single user case;

FIG. 4 b is a flow diagram showing design of optimized orthogonal modes for use in the system of FIG. 1 for a multi user system;

FIG. 5 is a flow diagram showing an embodiment of calculation of throughput at the transmitter in the system of FIG. 1 for single user case;

FIG. 6 is a flow diagram showing an embodiment of calculation of throughput at the transmitter in the system of FIG. 1 for multiple user case;

FIG. 7 is a flow diagram showing design of stored modulation matrices at the transmitter of FIG. 1 for multiple user case;

FIG. 8 is a flow diagram showing nested quantization;

FIG. 9 is a flow diagram showing design of mode gain matrixes for use in the system of FIG. 1;

FIG. 10 is a flow diagram showing design of power allocation matrices for use at the transmitter of FIG. 1 for multiple user case;

FIG. 11 shows the distribution of {circumflex over (V)} and Ŝ in vector codebooks for a 2×2 MIMO system and N_(V)=2 and N_(S)=1. The two orthogonal eigenvectors in each {circumflex over (V)} matrix are shown using the same line style. The X-axis corresponds to the real entries in the first rows of {circumflex over (V)} and the YZ-plane corresponds to the complex entries in the second rows of the matrices. In case of Ŝ, the entries are presented for different power levels P in a normalized form Ŝ/P. FIG. 11( a) shows {circumflex over (V)} for N_(V)=2 and FIG. 11( b) shows S for N_(S)=1

FIG. 12 shows (a) ergodic system capacity C and (b) average feedback bit rate R_(b) for different granularities of channel state information at the transmitter on the flat fading channel. System capacity is shown in logarithmic scale to better illustrate the differences between curves.

FIG. 13 shows (a) capacity loss and (b) average feedback bit rate R_(b) on the frequency selective channel.

FIG. 14 shows sum-rates of cooperative, zero-forcing DPC and linear systems with full CSIT and 10 users with identical receivers.

FIG. 15 is an example of multi-user interference caused by partial CSIT.

FIG. 16 is an example of the nested quantization of eigenmodes. The thick lines symbolize centroids {circumflex over (v)}(i) of the respective regions υ_(j). 16(a) shows a coarse 2-bit quantizer and 16(b) a precise 2-bit quantizer of υ₃.

FIG. 17 shows linear system sum-rates with full CSIT and varying feedback bit-rate for K=2. n_(T)=2 and all users with n_(R)=2 antennas.

FIG. 18 shows linear system sum-rates with full CSIT and varying feedback bit-rate for K=10. n_(T)=2 and all users with n_(R)=2 antennas.

FIG. 19 shows linear system sum-rates with full CSIT and nested CSI quantization with varying eigenmode coherence time τ_(eig) (N_(υ)) n_(T)=2, K=10, n_(R)=2, N_(s)=1 and N_(υ)=2, 4.

DETAILED DESCRIPTION

One of the fundamental issues in multiple antenna systems is the availability of the channel state information (CSI) at transmitter and receiver [24]. The perfect CSI at the transmitter (CSIT) enables the use of a spatial water-filling, dirty paper coding and simultaneous transmission to multiple users, allowing the systems to approach their maximum theoretical capacity. Such systems are usually referred to as closed-loop as opposed to open-loop systems where there is no feedback from the receiver. Closed-loop systems enable major increases of system capacities, allowing the operators to multiply their revenue and maintain high quality of service at the same time.

In this work, we describe a flexible approach to CSI encoding, which can be used to construct the linear modulation matrices for both single-user and multi-user networks. In both cases, the modulation matrices are composed of two independent parts: the eigenmode matrix and the diagonal power division matrix with the sum of entries on the diagonal equal to 1. The system operates as follows:

-   -   1. The receiver(s) [24] estimate(s) the respective multiple         antenna channel(s).     -   2. Each estimated channel is decomposed using the singular value         decomposition (SVD) to form the matrix of eigenmodes [30] and         their respective singular values [30A].     -   3. If the system works in the single-user mode, all entries in         the codebook of transmitter eigenmode modulation matrices [32]         and all entries in the codebook of transmitter power division         matrices [32A] are tested at the receiver to choose their         combination providing highest instantaneous capacity. The         indices of the best transmitter eigenmode and power division         matrices are then sent [34],[34A] back to the transmitter.

4. If the system works in the multi-user mode, all entries in the codebook of receiver eigenmode vectors [32] and all entries in the codebook of receiver mode gains [32A] are tested at the receiver [24] for best match with the estimated channel (the matching function can be chosen freely by the system designer). The indices of the best receiver eigenmode and power division matrices [94] are then sent [34] back to the transmitter.

5. Based on the received [36],[36A] indices, the transmitter chooses [38],[52],[62] the modulation matrix and uses it to transmit [40] the information to one or more users [24] at a time.

Our proposed method allows to simplify the feedback system by implementing only one set of eigenmode matrices for all values of signal-to-noise ratio (SNR) and a much smaller set of power division matrices that differ slightly for different values of SNR. As a result, the required feedback bit rate is kept low and constant throughout the whole range of SNR values of interest. The additional advantage of the splitting of the modulation matrix into two parts is that is can lower the feedback bit rate for slowly-varying channels. If the eigenmodes of the channel stay within the same region for an extended period of time, additionally, nested encoding can be performed to increase the resolution of the CSIT and improve the system capacity.

The actual design of the receiver and transmitter eigenmode and power division matrices can be done using numerical or analytical methods and is not the object of this disclosure. However, our method allows for actual implementations of systems closely approaching the theoretical capacities of MIMO channels without putting any unrealistic demand on the feedback link throughput. This is a major improvement compared to the other state-of-the art CSI quantization methods, which experience problems approaching the theoretical capacities and suffer from the early onset of capacity ceiling due to inter-user interference at relatively low SNR.

I System Model for Single User Communication and OFDM

We assume that the communication system consists of a transmitter equipped with n_(T) antennas [22] and a receiver [24] with n_(R) antennas [26]. A general frequency selective fading channel is modeled by a set of channel matrices H_(j) of dimension n_(R)×n_(T) defined for each sub-carrier j=0, 1, . . . N_(OFRM)−1. The received signal at the jth sub-carrier is then given by the n_(R)-dimensional vector y_(j) defined as

y _(j) =H _(j) x _(j) +n _(j)  (1)

where x_(j) is the n_(T)-dimensional vector of the transmitted signal and n_(j) is the n_(R)-dimensional vector consisting of independent circular complex Gaussian entries with zero mean and variance 1. Moreover, we assume that power is allocated equally across all sub-carriers |x_(j)|²=P.

II Quantizing Water-Filling Information

If the transmitter has access to the perfect channel state information about the matrix H_(j), it can select the signaling vector x_(j) to maximize the closed-loop system capacity

$\begin{matrix} {C = {\sum\limits_{j}{\log_{2}{\det \left\lbrack {I + {H_{j}Q_{j}H_{j}^{H}}} \right\rbrack}}}} & (2) \end{matrix}$

where Q_(j)=E[x_(j)x_(j) ^(H)]. Unfortunately, optimizing the capacity in (2) requires a very large feedback rate to transmit information about optimum Q_(j) (or correspondingly H_(j)) which is impractical. Instead, we propose using a limited feedback link, with the transmitter choosing from a set of matrices {circumflex over (Q)}(n).

Using the typical approach involving singular value decomposition and optimum water-filling, we can rewrite (1) as

y _(j) =H _(j) x _(j) +n _(j)(U _(j) D _(j) V _(j) ^(H))(V _(j) {tilde over (x)} _(j))n _(j)  (3)

where E[|{tilde over (x)}_(j){tilde over (x)}_(j) ^(H)]=S_(j) constrained with Tr (S_(j))=P is a diagonal matrix describing optimum power allocation between the eigenmodes V_(j). Based on (3), the set of matrices Q_(j)=V_(j)S_(j)V_(j) ^(H) maximizes capacity in (2).

To construct the most efficient vector quantizer for channel feedback, the straightforward approach would be to jointly optimize signal covariance matrices Q for all sub-carriers at once. Such an approach, however, is both complex and impractical, since any change of channel description and/or power level would render the optimized quantizer suboptimal. Instead, we propose an algorithm which separately quantizes information about eigemnode matrices V_(j) in codebook {circumflex over (V)} and power allocation S_(j) in codebook Ŝ. Note that the first variable depends only on channel description and not on the power level P which simplifies the design.

We optimize the quantizers {circumflex over (V)} and Ŝ for flat-fading case and we apply them separately for each sub-carrier in case of OFDM modulation. Although such an approach is sub-optimal, it allows a large degree of flexibility since different system setups can be supported with the same basic structure.

A Quantizing Eigenmodes

We assume that the receiver [24] has perfect channel state information (CSIR) and attempts to separate [30] the eigenmode streams {tilde over (x)}_(j) in (3) by multiplying y_(j) with U_(j) ^(H). However, if the transmitter uses quantized eigenmode matrix set with limited cardinality, the diagonalization of {circumflex over (x)}_(j) will not be perfect. To model this, we introduce a heuristic distortion metric which is expressed as

γ_(V)(n;H)=∥DV ^(H) {circumflex over (V)}(n)−D∥ _(F)  (4)

where {circumflex over (V)}(n) is the nth entry in the predefined set of channel diagonalization matrices and ∥·∥_(F) is the Frobenius norm. We omitted subscript entries j in (4) for the clarity of presentation.

We assume that n=0, 1, . . . 2^(N) ^(V) −1 where N_(V) is the number of bits per channel realization in the feedback link needed to represent the vectors {circumflex over (V)}(n). To design the quantizer using (4), we divide the whole space of channel realizations H into 2^(N) ^(V) regions V where

V _(i) ={H:γ _(V)(i;H) for all j≠i}  (5)

It can be shown that minimizing this metric should, on average, lead to maximizing the ergodic capacity of the channel with the quantized feedback (when γ(n; H)=0 the channel becomes perfectly diagonalized). The optimum selection of {circumflex over (V)} and regions V_(i) in (5) is an object of our current work. Here, however, we use a simple iterative heuristic based on a modified form of the Lloyd algorithm, which has very good convergence properties and usually yields good results. The algorithm starts by creating a codebook of centroids {circumflex over (V)} and, based on these results, divides the quantization space into regions V_(i). The codebook is created as follows: [50]

-   -   1. Create a large training set of L random matrices H(l). [46]     -   2. For each random matrix H(l), perform singular value         decomposition to obtain D(l) and V(l) as in (3).     -   3. Set iteration counter i=0. Create a set of 2^(N) ^(V) random         matrices Ĥ(n).     -   4. For each matrix Ĥ(n) calculate corresponding {circumflex over         (V)}^((i))(n) using singular value decomposition.     -   5. For each training element H(l) and codebook entry {circumflex         over (V)}^((i))(n) calculate the metric in (4). For every l         choose indexes n_(opt)(l) corresponding to the lowest values of         γ_(V) (n; H(l)).     -   6. Calculate a new set {circumflex over (V)}^((i+1))(n) as a         form of spherical average of all entries V(l) corresponding to         the same index n using the following method. (The direct         averaging is impossible since it does not preserve orthogonality         between eigenvectors.) For all n calculate the subsets L(n)={l:         n_(opt)(l)=n} and if their respective cardinalities |L(n)|≠0 the         corresponding matrices Q ^((i+1))(n) can be obtained as

$\begin{matrix} {{{\overset{\_}{Q}}^{({i + 1})}(n)} = {\frac{1}{{L(n)}}{\sum\limits_{l \in {L{(n)}}}{{V(l)}^{1}{{OV}(l)}^{H}}}}} & (6) \end{matrix}$

-   -   -   where ¹O is an n_(T)×n_(T) all-zero matrix with the             exception of the upperleft corner element equal to 1.             Finally, using singular value decomposition, calculate             {circumflex over (V)}^((i+1))(n) from

Q ^((i+1))(n)={circumflex over (V)} ^((i+1))(n)W({circumflex over (V)} ^((i+1))(n)^(H)  (7)

-   -   -   where W is a dummy variable.

    -   7. Calculate the average distortion metric γ _(V) ^((i+1))=1/L         Σ_(l)γ_(V)(n_(opt)(l); H(l)).

    -   8. If distortion metric fulfills | γ _(V) ^((i+1))− γ _(V)         ^((i))|/ γ _(V) ^((i))<Θ, stop. Otherwise increase i by 1 and go         to 5).

Upon completion of the above algorithm, the set of vectors {circumflex over (V)} can be used to calculate the regions in (5). The results of the codebook optimization are presented in FIG. 11 a) for a case of n_(T)=n_(R)=2 and N_(V)=2. The optimization was performed using L=1,000·2^(N) ^(V) and θ=10⁻⁷.

B Quantizing Power Allocation Vectors

Having optimized [50] power-independent entries in the codebook of channel eigenmode matrices V, the next step is to create a codebook for power allocation Ŝ[118]. We use a distortion metric defined as

$\begin{matrix} {{\gamma_{S}\left( {k;H;P} \right)} = \frac{\det \left\lbrack {I + {HQH}^{H}} \right\rbrack}{\det \left\lbrack {I + {H{\hat{V}\left( n_{opt} \right)}{\hat{S}(k)}{{\hat{V}}^{H}\left( n_{opt} \right)}H^{H}}} \right\rbrack}} & (8) \end{matrix}$

where Ŝ(k) is the kth entry in the predefined set of channel water-filling matrices and {circumflex over (V)}(n_(opt)) is the entry in the {circumflex over (V)} codebook that minimizes metric (4) for the given H. We use k=0, 1, . . . 2^(N) ^(S) −1 where N_(S) is the number of bits per channel realization in the feedback link needed to represent the vectors Ŝ(k). Minimizing the metric in (8) is equivalent to minimizing the capacity loss between the optimum water-filling using Q and the quantized water-filling using {circumflex over (V)} and Ŝ.

Similarly to the previous problem, we divide the whole space of channel realizations H into 2^(N) ^(s) regions S_(i)(P) where

S _(i)(P)={H:γ _(S)(i;H;P)<γ_(S)(j;H;P) for all j≠i}  (9)

and to create the codebook Ŝ, we use the following method:

-   -   1. Create a large training set of L random matrices H(l).     -   2. For each random matrix H(l), perform water-filling operation         to obtain optimum covariance matrices Q(l) and S(l).     -   3. Set iteration counter i=0. Create [100],[104] a set of 2^(N)         ^(s) random diagonal matrices Ŝ^((i))(k) with Tr (Ŝ^((i))(k))=P.     -   4. For every codebook entry Ŝ^((i))(k) and matrix Q(l).         calculate [112] the metric as in (8). Choose [106] indexes         k_(opt)(l) corresponding to the lowest values of γ_(S)(k; H(l);         P).     -   5. If γ_(S)(k_(opt)(l); H(l); P)>γ_(eq)(H(l); P) where         γ_(eq)(H(l), P) is the metric corresponding to equal-power         distribution defined as

$\begin{matrix} {{{\gamma_{eq}\left( {{H(l)};P} \right)} = \frac{\det \left\lbrack {I + {{H(l)}{Q(l)}{H^{H}(l)}}} \right\rbrack}{\det \left\lbrack {I + {{P/n_{T}}{H(l)}{H^{H}(l)}}} \right\rbrack}},} & (10) \end{matrix}$

-   -   -   set the corresponding entry k_(opt)(l)=2^(N) ^(S) . For all             k calculate the subsets [108] L(k)={l: k_(opt)(l)=k}.

    -   6. For all k=0, 1, . . . 2^(N) ^(s) −1[114] for which |L(k)|≠0,         calculate [116] a new set Ŝ^((i+1))(k) as the arithmetic average

$\begin{matrix} {{{\hat{S}}^{({i + 1})}(k)} = {\frac{1}{{L(k)}}{\sum\limits_{l \in {L{(k)}}}{{S(l)}.}}}} & (11) \end{matrix}$

-   -   7. Calculate the average distortion metric

$\begin{matrix} {{\overset{\_}{\gamma}}_{S}^{({i + 1})} = {\frac{1}{L}{\sum\limits_{l}{\min {\left\{ {{\gamma_{S}\left( {{k_{opt}(l)};{H(l)};P} \right)},{\gamma_{eq}\left( {{H(l)};P} \right)}} \right\}.}}}}} & (12) \end{matrix}$

-   -   8. If distortion metric fulfills | γ _(s) ^(i+1)− γ _(s) ^(i)|/         γ _(S) ^((i))<Θ, stop. Otherwise increase i with 1 and go to 4).

The set of vectors Ŝ is then used to calculate the regions in (9). Since water-filling strongly depends on the power level P and {circumflex over (V)}, optimally the Ŝ should be created for every power level and number of bits N_(V) in eigenvector matrix codebook. As an example, the results of the above optimization are presented in FIG. 11 b) for a case of n_(T)=n_(R)=2, N_(V)=2 and N_(S)=1. The optimization was performed using L=1,000·2^(N) ^(s) and Θ=10⁻⁷. The curves show the entries on the diagonals of the two matrices Ŝ(k) normalized with P. As one can see, one of the matrices tends to assign all the power to one of the eigenmodes, while the other balances the assignment between them. As expected, the balancing becomes more even with increasing P where the capacity of the equal-power open-loop systems approaches the capacity of the water-filling closed-loop systems. Since the differences between the entries of Ŝ(k) are not that large for varying powers, it is possible to create an average codebook Ŝ which could be used for all values of P but we do not treat this problem in here.

An interesting property of the above algorithm is that is automatically adjusts the number of entries in Ŝ according to the number of entries in {circumflex over (V)}. For low values of N_(V), even if the algorithm for selection of Ŝ is started with high N_(S), the optimization process will reduce the search space by reducing cardinality |L(k)| of certain entries to 0. As a result, for N_(V)=2, 3, N_(S)=1 will suffice, while for N_(V)=4, the algorithm will usually converge to N_(S)=2. This behavior can be easily explained since for low resolution of the channel eigenvector matrices {circumflex over (V)} only low precision is necessary for describing Ŝ. Only with increasing N_(V), the precision N_(S) becomes useful.

III VQ Algorithm for Flat-Fading MIMO Channels

The vector quantizers from the previous sections are first applied to a flat-fading channel case. In such a case, the elements of each matrix H in (1) are independent circular complex Gaussian elements, normalized to unit variance.

The system operation can now be described as follows:

-   -   1. The receiver [24] estimates the channel matrix H.     -   2. The receiver [24] localizes the region V according to (5) and         stores its index as n_(opt). [32]     -   3. Using n_(opt), the receiver [24] places H in a region S_(i)         according to (9) and stores its index as k_(opt).     -   4. If the resulting system capacity using the predefined         codebook entries is higher than the capacity of equal power         distribution as in

C(n _(opt) ,k _(opt))>log₂ det[II+P/n _(T) HH ^(H)]  (13)

-   -   -   indexes n_(opt) and k_(opt) are fed back to the transmitter.             [34], [36]

    -   5. The transmitter uses [40],[38A] the received indices of a         codebook entries to process its signal. If there is no feedback,         power is distributed equally between the antennas [22].

Using the above algorithm, the system's performance is lower-bounded by the performance of the corresponding open-loop system and improves if the receiver [24] finds a good match between the channel realization and the existing codebook entries. The salient advantage of such an approach is its flexibility and robustness to the changes of channel model. If there are no good matches in the codebook, the feedback link is not wasted and the transmitter uses the equal power distribution. The disadvantage of the system is that the feedback link is characterized by a variable bit rate.

IV VQ Algorithm for Frequency-Selective MIMO-OFDM Channels

In case of the frequency-selective channel, flat fading algorithm is applied to the separate OFDM sub-carriers. Although this approach is clearly sub-optimal, it allows us to use a generic vector quantizer trained to the typical flat-fading channel in a variety of other channels.

In general case, the feedback rate for such an approach would be upperbounded by N_(OFDM) (N_(V)+N_(D)). However, as pointed out by Kim et al, the correlation between the adjacent sub-carriers in OFDM systems can be exploited to reduce the required feedback bit rate by proper interpolating between the corresponding optimum signal ling vectors. In this work, we use a simpler method which allows the receiver [24] to simply group adjacent M sub-carriers and perform joint optimization using the same codebook entry for all of them (such methods are sometimes called clustering).

V Simulation Results A Flat-Fading Channel

We tested the system on 2×2 MIMO and 4×4 MIMO channels with varying SNR and feedback rates. We tested 2×2 MIMO channel with N_(V)=2, 3, 4 and N_(D)=1, corresponding to total feedback rate of between 3 and 5 bits. Correspondingly, in case of 4×4 MIMO, we used N_(V)=10, 12, 14 and N_(D)=2, corresponding to total feedback rate between 12 and 16 bits. We define an additional parameter called feedback frequency, ν which defines how often the receiver [24] requests a specific codebook entry instead of equal power distribution and an average feedback bit rate as R_(b)=ν(N_(V)+N_(S)).

FIG. 12( a) presents the results of simulations of ergodic capacity of the system (based on 100,000 independent channel matrices H) in case of perfect CSIT, vector quantized feedback (partial CSIT) and no CSIT. It is clearly seen that, even for very low bit rates on a feedback channel, the proposed scheme performs closely to the optimum. A rule of thumb seems to be that the number of bits needed to encode the codebook is approximately equal to n_(T)×n_(R). Moreover, FIG. 12( b) shows that as the SNR grows, less feedback is required to provide good system performance and the proposed algorithm automatically reduces the reverse link usage.

It is also interesting to note that increasing the quality of quantization increases the feedback frequency ν. This is a consequence of the fact that there is a higher probability of finding a good transmit signal covariance matrix when there are a lot of entries in the codebook.

B Frequency-Selective Channel

We have simulated the 2×2 MIMO system using the OFDM modulation with carrier frequency: f_(c)=2 GHz; signal bandwidth; B=5 MHz, number of sub-carriers: N_(OFDM)=256; ITU-R M.1225 vehicular A channel model with independent channels for all pairs of transmit and receive antennas [22][26]; the guard interval equal to the maximum channel delay.

The results of simulations are presented in FIG. 13. Since the capacity curves of this system are very similar to capacity of the flat-fading 2×2 system in FIG. 12 we decided to show the losses of performance as compared to the perfect water-filling case instead. FIG. 13 a) shows the loss of performance defined as C−C(M) where C is defined in (2) and C(N_(V), N_(S), M) is the capacity of the system with different feedback rates and clustering of M sub-carriers. As expected, increasing the clustering, decreases the throughput since the same covariance matrix is used for too many adjacent sub-carriers. At the same time, in FIG. 13 b) shows that the required average feedback rate decreases significantly with increasing M. For the simulated channel, the best results are provided by M=8, which is approximately equal to the coherence bandwidth of the channel. An interesting feature of the OFDM-MIMO is that, unlike in the flat-fading case, the feedback rate remains almost constant throughput the P range. In any case, however, around two orders of magnitude more feedback bit rate is required on frequency selective channel.

VI System Model for Multi User Communication

We assume that the communication system consists of a transmitter equipped with n_(T) antennas [22] and K≧n_(T) mobile receivers [24] with identical statistical properties and n_(R)(k) antennas [26], where k=1, 2, . . . K. The mobile user channels are modeled by a set of i.i.d. complex Gaussian channel matrices H_(k) of dimension n_(R)(k)×n_(T). (Throughout the document we use the upper-case bold letters to denote matrices and lower-case bold letters to denote vectors.) The received signal of the kth user is then given by the n_(R)(k)-dimensional vector y_(k) defined as

Y _(k) =H _(k) x+n _(k)  (14)

where x is the n_(T)-dimensional vector of the transmitted signal and n_(k) is the n_(R)(k)-dimensional vector consisting of independent circular complex Gaussian entries with zero means and unit variances. Finally, we assume that the total transmit power at each transmission instant is equal to P. The above assumptions cover a wide class of wireless systems and can easily be further expanded to include orthogonal frequency division multiplexing (OFDM) on frequency-selective channels or users with different received powers (due to varying path loss and shadowing).

Although theoretically it is possible to design the optimum CSI quantizer for the above canonical version of the system, such an approach may be impractical. For example, subsets of receivers [24] with different numbers of receive antennas[26] would require different CSI codebooks and quantizer design for such a system would be very complex. To alleviate this problem, we assume that the base station treats each user as if it was equipped with only one antenna [26], regardless of the actual number of antennas [26] it may have. While suboptimal, such an approach allows any type of a receiver [24] to work with any base station and may be even used to reduce the quantization noise as shown by Jindal. We call such system setup virtual multiple-input single-output (MISO) since, even though physically each transmitter-receiver link may be a MIMO link, from the base station's perspective it behaves like MISO.

We follow the approach of Spencer et al, where each user performs singular value decomposition of H_(k)=U_(k)S_(k)V_(k) ^(H) [30] and converts its respective H_(k), to a n_(r)-dimensional vector h_(k) as

h_(k)=u_(k) ^(H)H_(k)=s_(k) ^(max)v_(k) ^(H)  (15)

[42] where s_(k) ^(max) is the largest singular value [30A] of S_(k) and u_(k) and v_(k) are its corresponding vectors [30] from the unitary matrices U_(k) and V_(k), respectively.

Based on (15), the only information that is fed [36],[36A] back from [34],[34A] the receivers [24] to the transmitter is the information about the vectors h_(k), which vastly simplifies the system design and allows for easy extensions. For example, if multiple streams per receiver are allowed, the channel information for each stream can be quantized using exactly the same algorithm.

VII System Design with Full CSIT

In this section, we present typical approaches for the system design when full CSIT is available. As a simple form of multi-user selection diversity, we define a subset of active users with cardinality n_(T) as S. Furtheiniore, for each subset S, we define a matrix H[S]=[h₁ ^(T), h₂ ^(T), . . . , h_(nT) ^(T)]^(T), whose rows are equal to the channel vectors h_(k) of the active users.

A Cooperative Receivers

The upper-bound for system sum-rate is obtained when the users are assumed to be able to cooperate. With such an assumption, it is possible to perform singular value decomposition of the joint channel as H[S]=U[S]S[S]V^(H)[S]. Defining s_(i) as the entries on the diagonal of S[S] allows to calculate the maximum sum-rate of a cooperative system as

$\begin{matrix} {R^{coop} = {\max\limits_{}{\sum\limits_{i = 1}^{n_{T}}\left\lbrack {\log_{2}\left( {{\xi \lbrack \rbrack}s_{i}^{2}} \right)} \right\rbrack_{+}}}} & (16) \end{matrix}$

where ξ[S] is the solution of the water-filling equation

${\sum\limits_{i = 1}^{n_{T}}\left\lbrack {{\xi \lbrack \rbrack} - {1/s_{i}^{2}}} \right\rbrack_{+}} = {P.}$

B Zero-Forcing Dirty-Paper Coding

In practice, the receivers [24] cannot cooperate and the full diagonalization of the matrix H[S] is impossible. The problem can still be solved by using linear zero-forcing (ZF) followed by non-linear dirty paper precoding, which effectively diagonalizes the channels to the active users. The matrix H[S] is first QR-decomposed as H[S]=L[S]Q[S], where L[S] is lower triangular matrix and Q[S] is a unitary matrix. After multiplying the input vector x by Q[S]^(H), the resulting channel is equal to L[S], i.e., the first user does not suffer from any multi-user interference (MUI), the second user receives interference only from the first user, etc.

In this case, non-causal knowledge of the previously encoded signals can be used in DPC encoder allowing the signal for each receiver [24]i>1 to be constructed in such a way that the previously encoded signals for users k<i are effectively canceled at the ith receiver [24]. Since the effective channel matrix is lower triangular, the channel will be diagonalized after the DPC, with l_(i) being the entries on the diagonal of L[S]. This leads to maximum sum-rate calculation as

$\begin{matrix} {R^{{zf}\text{-}{dpc}} = {\max\limits_{^{ord}}{\sum\limits_{i = 1}^{n_{T}}\left\lbrack {\log_{2}\left( {{\xi \left\lbrack ^{ord} \right\rbrack}l_{i}^{2}} \right)} \right\rbrack_{+}}}} & (17) \end{matrix}$

where ξ[S^(ord)] is the solution of the water-filling equation. Note that, as opposed to (16), the maximization is performed over ordered versions of the active sets S.

C Linear Modulation

Even though, theoretically, the above approach solves the problem of the receiver [24] non-cooperation, its inherent problem is the absence of effective, low complexity DPC algorithms. Moreover, since dirty-paper coding requires full CSIT, it is likely that systems employing DPC would require significantly higher quality of channel feedback than simpler, linear precoding systems.

We use the linear block diagonalization approach, which eliminates MUI by composing the modulation matrix B[S] of properly chosen null-space eigenmodes for each set S. For each receiver [24]iεS, the ith row of the matrix H[S] is first deleted to form H[S_(i)]. In the next step, the singular value decomposition is performed [30],[30A] to yield H[S_(i)]=U[S_(i)]S[S_(i)]V^(H)[S_(i)]. By setting the ith column of B[S] to be equal to the rightmost vector of V[S_(i)], we force the signal to the ith receiver [24] to be transmitted in the null-space of the other users and no MUI will appear. In other words, the channel will be diagonalized with d_(i), being the entries on the diagonal of H[S]B[S]. This leads to formula

$\begin{matrix} {R^{linear} = {\max\limits_{}{\sum\limits_{i = 1}^{n_{T}}\left\lbrack {\log_{2}\left( {{\xi \lbrack \rbrack}d_{i}^{2}} \right)} \right\rbrack_{+}}}} & (18) \end{matrix}$

where ξ[S] is the solution of the water-filling equation.

As an example, FIG. 14 shows the sum-rates of the discussed systems for K=10 users and different antenna configurations. The zero-forcing DPC system approaches the cooperative system's sum-rate as the transmitted power increases. The sub-optimal linear modulation provides lower sum-rate but losses at P>0 dB, as compared to the ZF-DPC system, are in the range of only 1-2 dB for the 4×4 configuration and fractions of dB for the 2×2 system. Since the linear system is much easier to implement than ZF-DPC, we will use it to test our CSI encoding algorithms.

VIII System Design with Partial CSIT

The systems discussed so far are usually analyzed with assumption that, at any given time, the transmitter will have full information about the matrices H[S]. Unfortunately, such an assumption is rather unrealistic and imperfect CSIT may render solutions relying on full CSIT useless.

In practice, the receivers [24] will quantize the information about their effective channel vectors h_(k) [30] as ĥ_(k) [32], according to some optimization criterion. Based on this information, the transmitter will select [38],[52],[62] the best available modulation matrix B from the predefined transmitter codebook and perform water-filling using the best predefined power division matrix {circumflex over (D)}. Regardless of the optimization criterion, the finite cardinality of the vector codebooks will increase MUI and lower system throughput. FIG. 15 shows the mechanism leading to appearance of the MUI in a simple system with n_(T)=2 and K=2 users with effective channel vectors h₁ and h₂, encoded by the quantizer as ĥ₁ and ĥ₂. If the full CSIT is available, the transmitter will choose [38] a modulation matrix based on eigenmodes v₁ and v₂, which are perpendicular to vectors h₂ and h₁, respectively. As a result, each user will be able to extract its desired signal without MUI. With partial CSIT, however, the transmitter will obtain only approximate versions of the effective channel vectors, and the resulting modulation matrix will be based on eigenmodes {circumflex over (v)}₁ and {circumflex over (v)}₂, whose dot products with h₂ and h₁ will not be zero, causing the MUI.

IX CSI Quantization Algorithms

The fundamental difference between CSI encoding in single-user and multiple-user systems is that during normal system operation, each receiver [24] chooses its vector ĥ_(k) without any cooperation with other receivers [24]. This means that the design of optimum codebook for h_(k), must precede the design of codebooks {circumflex over (B)} and {circumflex over (D)}. Based on (15), one can see that channel state information in form of the vector h_(k) consists of the scalar value of channel gain [30A] s_(k) ^(max) and the eigenmode [30] v_(k) ^(H). Since these values are independent, we propose an algorithm which separately quantizes the information about eigenrnodes [32] in codebook {circumflex over (v)} and amplitude gains [32A] in codebook ŝ.

A Quantization [32],[51] of Receiver Channel Eigenmodes

We assume that N_(υ) is the number of bits per channel realization in the feedback link needed to represent the vectors v_(k) in (15). We divide the space of all possible v's into 2^(N) ^(υ) regions υ_(i)

υ_(i) ={v:γ _(υ)(i;<γ _(υ)(j;v) for all j≠i}  (19)

where γ_(V)(n; v) is a distortion function. Within each region υ_(i), we define a centroid vector {circumflex over (v)}(i) [49], which will be used as a representation of the region. The design of the codebook {circumflex over (v)} can be done analytically and/or heuristically using for example the Lloyd algorithm. In this work, we define the distortion function as the angle between the actual vector v and {circumflex over (v)}(i): γ_(υ)(i; v)=cos⁻¹({circumflex over (v)}(i) ·v), which has been shown by Roh and Rhao to maximize ergodic capacity, and use Lloyd algorithm to train [47] the vector quantizer. Note that the construction of {circumflex over (v)} is independent of the transmit power.

B Quantization [32A] of Receiver Amplitude Gains

We assume that N_(s) is the number of bits per channel realization in the feedback link needed to represent the scalar s_(k) ^(max) in (15). We divide the space of all possible channel realizations s=s^(max) into 2^(N) ^(s) regions s_(i)

s _(i) ={s:|ŝ(i)−s|<ŝ(j)−s| for all j≠i}  (20)

where ŝ(i) [100] are scalar centroids representing regions s_(i). In this work, we perform the design [102] of the codebook ŝ using the classical non-uniform quantizer design algorithm with distortion function given by quadratic function of the quantization error as ε(i; s)=(s−,ŝ(i))².

The construction of the codebook ŝ is generally dependent on the transmit power level. However, as pointed out above the differences between the codebooks ŝ for different power regions are quite small. This allows us to create only one codebook ŝ and use it for all transmit powers.

C Quantization of the Transmitter Modulation Matrices

The calculation of the modulation matrix {circumflex over (B)} is based on the given codebook {circumflex over (v)}. We assume that the quantization [32] of the channel eigenmodes is performed at the receiver [24] side and each user transmits [34] back its codebook index i_(k). The indices are then used at the transmitter side to select [38][52][62] the modulation matrix {circumflex over (B)}(i₁, i₂, . . . i_(K)). Since, from the linear transmitter point of view, ordering of the users is not important, we will use the convention that the indices (i₁, i₂, . . . i_(K)) are always presented in the ascending order. For example, in a system with K=2, n_(T)=2 and 1-bit vector quantizers {circumflex over (v)}, there will exist only three possible modulation matrices corresponding to sets of {circumflex over (v)} indices (1, 1), (1, 2) and (2, 2).

In the context of vector quantizing, the design of the modulation matrices can no longer be based on the algorithm presented in Section VII.C. Using this method with quantized versions of h_(k) produces wrong result when identical indices i_(k) are returned and the receiver [24] attempts to jointly optimize transmission to the users with seemingly identical channel vectors ĥ_(k). Instead, we propose the following algorithm to optimize the set of matrices {circumflex over (B)}(i₁, i₂, . . . i_(K)):

-   -   1. Create a large set of Nn_(T) random matrices [46] H_(k),         where N is the number of training sets with n_(T) users each.     -   2. For each random matrix H_(k), perform singular value         decomposition [68] and obtain h_(k) [70] as in (15).     -   3. For each vector h_(k) store [74] the index i_(k) of the         corresponding entry {circumflex over (v)}(i_(k)).     -   4. Divide [76] the entire set of matrices H_(k) into N sets with         n_(T) elements each.     -   5. Sort [78] the indices i_(k) within each set l in the         ascending order. Map [78] all unique sets of sorted indices to a         set of unique indices I_(B) (for example (1, 1)→I_(B)=1; (1,         2)→I_(B)=2; (2, 2)→I_(B)=3 . . . ).     -   6. In each set l, reorder the corresponding channel vectors         h_(k) according to their indices i_(k) and calculate [80] the         optimum B_(l) using the method from Section VII.C.     -   7. Calculate [84] a set {circumflex over (B)}(I_(B)) as a         column-wise spherical average of all entries B_(l) corresponding         to the same [82] index I_(B).

After calculation of |I_(B)| modulation matrices {circumflex over (B)}, the remaining part of system design is the calculation of the water-filling matrices {circumflex over (D)}, which divide the powers between the eigenmodes at the transmitter. The procedure for creation of codebook {circumflex over (D)}[118] is similar to the above algorithm, with the difference that the entries ŝ(n_(k)) are used instead of {circumflex over (v)}(i_(k)), and the spherical averaging of the water-filling matrices is performed diagonally, not column-wise. Explicitly:

-   -   1. Create a large set of Nn_(T) random matrices [46]H_(k), where         N is the number of training sets with n_(T) users each.     -   2. For each random matrix H_(k), perform singular value         decomposition [104] and obtain h_(k) as in (15).     -   3. For each vector h_(k) store the index n_(k) of the         corresponding entry ŝ(n_(k)). [106]     -   4. Divide the entire set of matrices H_(k) into N sets with         n_(T) elements each. [08]     -   5. Sort the indices n_(k) within each set l in the ascending         order. Map all unique sets of sorted indices to a set of unique         indices I_(D) (for example (1, 1)→I_(D)=1; (1, 2)→I_(D)=2; (2,         2)→I_(D)=3 . . . ). [110]     -   6. In each set l, reorder the corresponding channel vectors         h_(k) according to their indices n_(k) and calculate the optimum         D_(l) using the method of water-filling from Section VII.C.         [112]     -   7. Calculate [116] a set {circumflex over (D)}(I_(D)) as a         diagonal spherical average of all entries D_(l) corresponding to         the same [114] index I_(D).

D System Operation

The matrices {circumflex over (B)} and {circumflex over (D)} are used in the actual system in the following way:

-   -   1. The K mobile receivers [24] estirnate [30],[30A] their         channels and send the indices i_(k) [34] and n_(k) [34A] of the         corresponding receiver quantizer entries {circumflex over         (v)}(i_(k)) [32] ands(n_(k)) [32A] to the base station.     -   2. The transmitter forms l sets of users corresponding to all         combinations of ri_(T) users out of K. Within each set l, the         indices i_(k) [58] and n_(k) [63] are sorted in the ascending         order and mapped to their respective indices I_(B) (l) [60] and         I_(D)(l) [64];     -   3. Within each set l, the matrices {circumflex over         (B)}[I_(B)(l)][52],[62] and {circumflex over         (D)}[I_(D)(l)][54],[38A] are used to estimate [56], [66]         instantaneous sum-rate R(l).     -   4. The base station flags the set of users providing highest         R(l) as active for the next transmission epoch.     -   5. The transmitter uses the selected matrices to transmit         information.

E Nested Quantization of Channel Eigenmodes

The above algorithm does not assume any previous knowledge of the channel and the feedback rate required to initially acquire the channel may be high. In order to reduce it on slowly varying channels, we propose a nested quantization method shown in FIG. 16. In this example, an initial coarse quantization of the CSI is performed [88] using only 2 bits. Assuming [98] that the actual channel vector lies in region υ₃ and that it stays within this region during the transmission of subsequent frames [90],[92],[94], it is possible to further quantize υ₃ using nested, precise quantization [96]. In this way, the effective feedback rate is still 2 bits, but the resolution corresponds to a 4-bit quantizer. In order to quantify the possibility of such a solution, we introduce eigenmode coherence time τ_(eig) (N_(υ)), which, related to the frame duration T_(frame), shows for how long the channel realization stays within the same region υ_(i) of the N_(υ)-bit quantizer. Notice that eigenmode coherence time depends on the number of bits N_(υ): the higher the initial VQ resolution, the faster the channel vector will move to another region and the benefits of nested quantization will vanish.

X Simulation Results

We have implemented our system using a base station with fly=2 and a set of K mobile receivers [24] with identical statistical properties and n_(R)(k)=n_(T)=2. We have varied the number of users from 2 to 10 and optimized vector quantizers using methods presented above. Each system setup has been simulated using 10,000 independent channel realizations.

FIGS. 17 and 18 show the results of the simulations for varying feedback rates. Except for very high transmit power values P>15 dB, it is possible to closely approach the sum-rate of the full CSI system with 8 bits (N_(υ)=7, N_(s)=1) in the feedback link. The required feedback rate N_(υ), is much higher than N_(s), which shows the importance of high quality eigenmode representation in multi-user systems. In high power region, increasing N_(υ) by 1 bit increases the spectral efficiency by approximately 1 bit/channel use.

FIG. 19 shows the results of nested quantization with low feedback rates when the channel's eigenmode coherence time is longer than frame duration. If such a situation occurs, the considered system may approach the theoretical full CSIT sum-rate using only 5 bits per channel use in the feedback link.

Note that further feedback rate reduction can be achieved with the algorithm presented by Jindal. However, we will not present these results here.

XI Additional Matter

In case of multiple user systems, multi-user diversity may be achieved by a simple time-division multiplexing mode (when only one user at a time is given the full bandwidth of the channel) or scheduling the transmission to multiple users [24] at a time. Here we analyze the former approach and assume that the base station will schedule only one user [24] for transmission.

If the system throughput maximization is the main objective of the system design, the transmitter must be able to estimate [56] the throughput of each of the users, given the codebook indices it received from each of them. Assuming that the kth user returned indices requesting the eigenmode codeword {circumflex over (V)}_(k) and power allocation codeword Ŝ_(k), the user's actual throughput is given as

R _(k) ^(single)=log₂ det[II _(n) _(R) _((k)) +H _(k) {circumflex over (V)} _(k) Ŝ _(k) {circumflex over (V)} _(k) ^(H) H _(k) ^(H)].  (21)

Using singular value decomposition of channel matrix H_(k) and equality det[II_(n) _(R) _((k))+H_(k){circumflex over (Q)}_(k)H_(k) ^(H)]d=det[II_(n) _(T) +({circumflex over (Q)}_(k)H_(k) ^(H)H_(k)], it can be shown that

R _(k) ^(single)=log₂ det[II _(n) _(T) +{circumflex over (V)} _(k) Ŝ _(k) {circumflex over (V)} _(k) ^(H) H _(k) ^(H) H _(k)]=log₂ det[II _(n) _(R) _((k)) Ŝ _(k) E _(k) ^(H) D _(k) ^(H) E _(k)]  (22)

where E_(k)=V_(k) ^(H){circumflex over (V)}_(k) is a matrix representing the match between the actual eigenmode matrix of the channel and its quantized representation (with perfect match E_(k)=II_(n) _(T) ).

In practice, the actual realization of E_(k) will not be known at the transmitter, and its mean quantized value Ê_(k), matched to {circumflex over (V)}_(k), must be used instead. Similarly, the transmitter must use a quantized mean value {circumflex over (D)}_(k), which is matched to the reported waterfilling matrix Ŝ_(k). This leads to the selection criterion for the optimum user [24]k_(opt)

$\begin{matrix} {k_{opt} = {\arg \; {\max\limits_{{k = 1},2,{\ldots \mspace{14mu} K}}{\log_{2}{{\det \left\lbrack {I_{n_{R}{(k)}} + {{\hat{S}}_{k}{\hat{E}}_{k}^{H}{\hat{D}}_{k}^{2}{\hat{E}}_{k}}} \right\rbrack}.}}}}} & (23) \end{matrix}$

Similarly to single-user selection, also in the case of multi-user selection the choice of active users must be made based on incomplete CSIT. The quantized CSI will result in appearance of multi-user interference. We represent this situation using variable Ê_(k,l)={circumflex over (v)}_(k) ^(H)[{circumflex over (B)}_(S)]._(,l), which models the dot product of the quantized eigenmode {circumflex over (v)}_(k) ^(H) reported by the kth user in the set S, and the lth vector in the selected modulation matrix [52] {circumflex over (B)}_(S).

Moreover, assuming that the quantized singular value of the kth user in the set S is given by {circumflex over (d)}_(k), and the transmitter uses power allocation matrix [54] Ŝ_(S), the estimated sum-rate of the subset S is given as [56]

$\begin{matrix} {{R^{multi}()} = {\sum\limits_{k \in }{{\log_{2}\left( {1 + \frac{P{{\hat{d}}_{k}^{2}\left\lbrack {\hat{S}}_{} \right\rbrack}_{k,k}{\hat{E}}_{k,k}^{2}}{1 + {P{\hat{d}}_{k}^{2}{\sum\limits_{l \neq k}{\left\lbrack {\hat{S}}_{} \right\rbrack_{l,l}{\hat{E}}_{k,l}^{2}}}}}} \right)}.}}} & (24) \end{matrix}$

Note that, due to the finite resolution of the vector quantizer, the multi-user interference will lower the max sum-rate R^(multi)(S)<R^(max) for all S.

Based on (24) the choice of the active set of users is then performed as

$\begin{matrix} {_{opt} = {\underset{}{\arg \;}\max \; {{R^{vq}()}.}}} & (25) \end{matrix}$

One can also modify the algorithm presented in section II.A as follows: we use a simple iterative heuristic based on a modified form of the Lloyd algorithm, which has very good convergence properties. The algorithm starts by creating a random codebook of centroids {circumflex over (V)} and iteratively updates it until the mean distortion metric changes become smaller than a given threshold.

The algorithm works as follows:

-   -   1. Create a large training set of L random matrices H_(l). [46]     -   2. For each random matrix H_(l), perform singular value         decomposition to obtain V_(l) as in (3).     -   3. Align orientation of each vector in V_(l) to lie within the         same 2n_(T)-dimensional hemisphere.     -   4. Set iteration counter i=0. Create a set of 2^(N) ^(V) random         matrices Ĥ(n).     -   5. For each matrix Ĥ(n), calculate corresponding {circumflex         over (V)}(i) (n) using singular value decomposition.     -   6. Align orientation of each vector in {circumflex over         (V)}^((i))(n) to lie within the same 2n_(T)-dimensional         hemisphere.     -   7. For each training element H_(l) and codebook entry         {circumflex over (V)}^((i)) (n), calculate the metric in (4).         For every l, choose the index n_(opt)(l) corresponding to the         lowest value of γ_(V)(n; H_(l)). Calculate the subsets L(n)={l:         n_(opt) (l)=n} for all n.     -   8. Calculate new matrix if {circumflex over (V)}^((i+1))(n) as a         constrained spherical average V_(l) ^(◯) of all entries V_(l)         corresponding to the same index n

{circumflex over (V)} _((i+1))(n)= V _(l) ^(◯)|_(lεL(n)).  (26)

-   -   9. For each region n, where cardinality |L(n)|≠0, calculate the         mean eigenmode match matrix

$\begin{matrix} {{{\hat{E}}^{({i + 1})}(n)} = {\frac{1}{{L(n)}}{\sum\limits_{l \in {L{(n)}}}{V_{l}^{H}{{{\hat{V}}^{({i + 1})}(n)}.}}}}} & (27) \end{matrix}$

-   -   10. Calculate the average distortion metric γ _(V) ^((i+1))=1/L         Σ_(l)γ_(V)(n_(opt)(l), h_(l)).     -   11. If the distortion metric fulfills | γ _(V) ^((i+1))− γ _(V)         ^((i)))|/ γ _(V) ^((i))<Θ, where Θ is a design parameter, stop.         Otherwise increase i by 1 and go to 7).

Upon completion of the above algorithm, the final set of vectors V can be used to calculate the regions V_(i) in (5).

The design of the transmitter modulation matrices presented in section IX.C can be modified as follows: we propose the following algorithm to optimize the set of matrices {circumflex over (B)}(i₁, i₂ . . . i_(n) _(T) ):

-   -   1. Create a large set of Ln_(T) random matrices H_(l), where L         is the number of training sets with n_(T) users each.     -   2. For each random matrix H_(l), perform singular value         decomposition [68] and obtain h_(l)[70] as in (15).     -   3. Align orientation of each vector h_(l) to lie within the same         2n_(T)-dimensional hemisphere.     -   4. For each vector h_(l), store [74] the index i_(l) of the         corresponding entry {circumflex over (v)}(i_(l)).     -   5. Divide [76] the entire set of matrices H_(l) into L sets with         n_(T) elements each.     -   6. Sort [78] the indices i_(l) within each set in the ascending         order. Map [78] all unique sets of sorted eigenmode indices         i_(l) to a set of unique modulation matrix indices I_(B) (for         example, if n_(T)=2: (1, 1)→I_(B)=1; (1, 2)→I_(B)=2; (2,         2)→I_(B)=3 . . . ).     -   7. In each set L(I_(B))={l: (i₁, i₂, . . . i_(n) _(T) ) I_(B)},         reorder the channel vectors h_(l) according to the indices i_(l)         and calculate [80] the optimum B_(l) using the method from         Section VII.C.     -   8. Calculate [84] the set {circumflex over (B)}(I_(B)) as a         column-wise spherical average of all entries B_(l) corresponding         to the same [82] index I_(B) as

∀_(n=1, 2, . . . n) _(T) [{circumflex over (B)}(I _(B))]._(,n)= [B _(l)]._(,n) ^(◯)|_(L(I) _(B) ₎.  (28)

After completion of the above algorithm, the transmitter will have the set of |I_(B)| modulation matrices {circumflex over (B)}(I_(B)) corresponding to all sorted combinations of the channel eigenmode indices that can be reported by the receivers.

To clarify our notation for spherical average used in (26) and (28), we outline a method to calculate a spherical average of a set of unit-length vectors, and a spherical average of a set of unitary matrices, preserving the mutual perpendicularity of the component vectors. We use the notation v_(l) ^(◯)|_(lεL) to represent a spherical average of all unit-length vectors v_(l) contained in a set L. Based on Statistical Analysis of Spherical Data by Fisher et al, we define the spherical average as

$\begin{matrix} {{{\overset{\_}{v_{l}}}^{◯}}_{l \in L} = {\min\limits_{x}{\sum\limits_{l \in L}{\cos^{- 1}\left( {v_{l} \cdot x} \right)}}}} & (29) \end{matrix}$

where the unit-length vector x is found using one of the constrained non-linear optimization algorithms.

In case of the spherical average of a set of unitary matrices, denoted as V_(l) ^(◯)|_(lεL), the averaging of the unit-length column vectors must be performed in a way that the resulting matrix is also unitary. We represent the spherical matrix average as a collection of unit-length vectors x_(l) as V ^(◯)=[x₁, x₂, x₃, . . . ] and jointly optimize them as

$\begin{matrix} \left\{ \begin{matrix} {{x_{k} = {\min\limits_{x}{\sum\limits_{l \in L}{\cos^{- 1}\left( {\left\lbrack V_{l} \right\rbrack_{\cdot {,k}} \cdot x} \right)}}}},{k = 1},2,{3\mspace{14mu} \ldots}} \\ {{{x_{k} \cdot x_{l}} = 0},{k \neq {l.}}} \end{matrix} \right. & (30) \end{matrix}$

Immaterial modifications may be made to the embodiments described here without departing from what is covered by the claims. 

1. A method of transmission over multiple wireless channels in a multiple antenna system, the method comprising the steps of: storing channel modulation matrices at a transmitter; receiving quantized channel state information at the transmitter from plural receivers; selecting a transmission modulation matrix using the quantized channel state information from the stored channel modulation matrices; and transmitting over the multiple channels to the plural receivers using the selected transmission modulation matrix.
 2. The method of claim 1 in which the quantized channel state information comprises indexes identifying channel state modes.
 3. The method of claim 2 further comprising the steps of: receiving quantized mode gain information at the transmitter from the plural receivers; and adjusting transmission power allocation to the transmitter antennas using the quantized mode gain information.
 4. The method of claim 1 further comprising the step of storing at the receivers indexes of channel eigenmodes generated by a capacity enhancing algorithm.
 5. A method of transmission over multiple wireless channels in a multiple antenna system, the method comprising the steps of: storing channel modulation matrices at a transmitter; storing at one or more receivers indexes of modulation matrices generated by a capacity enhancing algorithm; upon a selected one of the one or more receivers receiving a transmission from the transmitter, the selected receiver selecting a modulation matrix from the stored modulation matrices that optimizes transmission between the transmitter and the selected receiver; the selected receiver sending an index representing the selected modulation matrix; receiving the index at the transmitter from the selected receiver; and the transmitter transmitting over the multiple channels using the selected transmission modulation matrix.
 6. The method of claim 5 further comprising the step of the transmitter, after receiving channel information from multiple receivers, choosing one particular receiver to transmit to for some period of time based on the expected throughput to each receiver.
 7. A method of transmission between a transmitter and one or more receivers in a wireless multiple channel system where the transmitter transmits on at least n>1 antennas, the method comprising the steps of: each of one or more receivers identifying orthogonal modes of the respective channels between the transmitter and the respective receiver by estimating channel coefficients, storing the channel coefficients in a channel matrix and performing a transformation of the channel matrix; each receiver quantizing the orthogonal modes of the channel by selecting a codebook index of a stored orthogonal mode matrix or a set of codebook indices of orthogonal mode vectors; each receiver transmitting the codebook index or codebook indices to the transmitter; and upon receiving the codebook index or codebook indices from the receivers, the transmitter selecting a group of k receivers, where k is selected from 1 up to and including n, and transmitting to the group of k receivers using a stored channel modulation matrix that is selected based upon the codebook index or codebook indices received by the transmitter.
 8. The method of claim 7 in which the stored channel modulation matrix comprises a full matrix of orthogonal modes or a matrix of non-orthogonal modes.
 9. The method of claim 8 in which, if a receiver of the one or more receivers receives a signal modulated using the full orthogonal mode matrix, the received signal is demodulated using a complementary full orthogonal mode matrix, and, if a receiver of the one or more receivers receives a signal modulated using the matrix of non-orthogonal modes, the received signal is demodulated using corresponding complementary orthogonal mode vectors.
 10. (canceled)
 11. The method of claim 7 in which at least one of condition A and condition B occurs where: A is the condition that each receiver stores a set of orthogonal mode matrices that have been created by creating a list of possible orthogonal mode matrices, and by selecting a capacity enhancing set of orthogonal mode matrices, for a given quantization resolution, from the possible orthogonal mode matrices; and B is the condition that each receiver stores a set of orthogonal mode vectors that have been created by creating a list of possible orthogonal mode matrices, separating the orthogonal mode matrices into component orthogonal mode vectors, and by selecting a distortion reducing set of orthogonal mode vectors, for a given quantization resolution, from the component orthogonal mode vectors.
 12. (canceled)
 13. The method of claim 7 in which: if the transmitter receives a codebook index corresponding to a full orthogonal mode matrix, the transmitter selects a stored channel modulation matrix based upon the received codebook index, selects a corresponding eigenmode distortion matrix and calculates estimated throughput for the receiver, and if the transmitter receives codebook indexes corresponding to orthogonal mode vectors, the transmitter maps the indices to a composite modulation index, uses the composite modulation index to select a corresponding stored channel modulation matrix, selects a corresponding distortion matrix and calculates estimated throughput for the k receivers.
 14. The method of claim 13 in which, if the transmitter receives codebook indexes corresponding to orthogonal mode vectors, the choice of composite modulation index is independent of the order of the list of codebook indices.
 15. The method of 14 in which the independence is ensured by ordering the indices and assigning a composite modulation index to each such ordered list of indices.
 16. The method of claim 11 in which each receiver has codebook indexes of orthogonal mode matrices and the stored channel modulation matrices at the transmitter are the same as the orthogonal mode matrices.
 17. The method of claim 11, in which each receiver has codebook indexes of orthogonal mode vectors, and the stored channel modulation matrices at the transmitter are constructed by: create a large list of possible orthogonal mode matrices; separating the orthogonal mode matrices into component orthogonal mode vectors; for each orthogonal mode vector, storing the codebook index of a matching pre-quantized mode vector from the codebook of orthogonal mode vectors at the receiver; forming all possible subsets of the stored codebook indices; within each subset mapping the stored codebook indices as a composite modulation index; for each subset, calculating the optimum channel modulation matrix based on the corresponding orthogonal mode vectors; grouping all the calculated optimum channel modulation matrices according to the corresponding composite modulation indices; and calculating the spherical average of all orthogonal mode vectors within channel modulation matrices in each group and store the spherical averages in a channel modulation matrix that is mapped to the selected composite modulation index.
 18. The method of claim 17 in which the choice of composite modulation index is independent of the order of the stored codebook indices.
 19. The method of claim 18 in which this independence is obtained by ordering the indices and mapping the ordered set of indices to a composite modulation index.
 20. (canceled)
 21. The method of claim 7 in which the transmitter adjusts power applied to the antennas at the transmitter based on received mode gain information from one or more receivers.
 22. The method of claim 21 further comprising each of the one or more receivers: selecting a transformed component of the channel matrix that contains mode gains of the channels, the transformed component being either treated as a matrix or an ensemble of scalar gain values; quantizing the transformed component by selecting from a stored codebook of gain matrices or gain values; and forwarding the quantized gain matrix or gain values to the transmitter as mode gain information.
 23. The method of claim 22 in which the transmitter adjusts power applied to the antennas at the transmitter based upon improving system throughput performance.
 24. The method of claim 23 in which system throughput performance is calculated by the transmitter: forming subsets of receivers; within each subset of receivers, mapping the mode gain values as a composite power allocation index; using the composite power allocation index to select a corresponding power allocation matrix from a set of stored power allocation matrices; based on the selected power allocation matrix, choose a corresponding average gain matrix; and based on the average gain matrices and knowledge of transmit power level at the receivers, calculate estimated throughput for the subset of receivers.
 25. The method of claim 24 in which the choice of composite power allocation index is independent of the order of the mode gain values.
 26. The method of 25 in which this independence is obtained by ordering the mode gain values and mapping the ordered set of values to a composite index.
 27. The method of claim 24 in which the stored codebook of gain matrices or gain values is formed by: if the full mode gain matrix is to be quantized, creating a large list of possible mode gain matrices and selecting a set of mode gain matrices; and if gain values are to be quantized, creating a large list of possible mode gain matrices, separating the mode gain matrices into their component scalar values and performing probabilistic scalar quantization with a selected resolution to select a set of mode gain values.
 28. (canceled)
 29. The method of claim 27 in which stored power allocation matrices at the transmitter are created by: creating a large list of possible scalar mode gains; for each possible scalar mode gain, storing the codebook gain index of a matching pre-quantized mode gain value; forming all possible subsets of the stored codebook gain indexes; within each subset, mapping the stored codebook gain indexes as a composite power allocation index; for each subset, calculating the power allocation matrix based on the corresponding mode gains; grouping all the calculated power allocation matrices according to the corresponding composite power allocation indexes; and calculating the average of all gain values within matrices in each group and storing the gain values in a power allocation matrix that is mapped to the selected composite power allocation index.
 30. The method of claim 29 in which the choice of composite power allocation index is independent of the order of the gain indexes.
 31. The method of 30 in which this independence is obtained by ordering the gain indexes and mapping the ordered set to a composite index. 32-35. (canceled)
 36. The method of claim 7 comprising the further step of having/using a more detailed codebook in addition to the ordinary codebook, with each entry in the original codebook corresponding to multiple entries in the more detailed codebook, such that if the channel is expected to stay sufficiently close to its previous state over the time period between sendings of feedback that the entry from the coarse codebook will not change, the receiver, instead of sending a new index from the coarse codebook, sends the index of an entry of the fine codebook in the subset of the codebook corresponding to the previously sent entry of the coarse codebook. 37-39. (canceled)
 40. The method of claim 7 in which the system transmits using OFDM, and the method is applied to sets of subcarriers jointly. 41-44. (canceled) 